This strategy, however, is suboptimal: The question generator needs to generate probabilities for items that do not lie on the same distribution (the distribution of natural language words vs. the distribution of binary decisions ask/guess).

we analyse the dialogues produced by the models in terms of the amount of repetitions they contain.


size of the target object, which we compute in terms of the proportion of the cropped target object area with respect to the whole image.

table 3: Percentages of repeated questions in all games and in decided games.


As stated in the introduction, we believe that a good visual dialogue model should not only be measured in terms of its task success but also with respect to the quality of its dialogues.

the dialogue manager is the core component of a dialogue agent: it integrates the semantic content produced by the interpretation module into the agent's representation of the context (the dialogue state) and determines the next action to be performed by the agent, which is transformed into linguistic output by the generation module.


To further encourage diverse generation, we incorporate a novel diversity rank into the utterance representation.

the second and third column in table 4 confirm this by showing less newly and diversely generated delexicalised utterances.


plain Siamese model, without any dialogue act information, has an MRR of 0.8 compared to 0.58 of the best performing generative model, i.e.

let h (1) and h (2) be the representations obtained from the first encoder and second encoder, respectively, then the probability of their association can be computed using the following expression.


In the case of Directive, we see as much as 15 percentage point improvement.

we need to modify the dataset in such a way that we only consider those turns where we need to predict the bot's utterance.


During decision making, these sub-agents have internal message exchange between neighbors on the graph.

figure 3: An example of dialogue on semantic level with a policy of GNN-A in CR domain (Env.1).


We compare our JTAV framework with: • doc2vec (Le and Mikolov, 2014), a paragraph embedding approach; • lyricsHAN (Alexandros, 2017), a hierarchical attention network which encodes songs into vectors; • BiGRU, a bidirectional GRU network which utilizes the song lyrics as input; • attBiGRU, our novel text modeling approach which adds the available captions as the supplying role based on BiGRU; • MFCC (McFee et al., 2017), a type of cepstral representation of the audio clip generated by librosa; • convnet (Choi et al., 2017a), an acoustic feature learning method based on transfer learning; • DCRNN-MelS, our novel audio modeling approach which takes MelSs as input; • DCRNN-CQT, our novel audio modeling approach which takes CQTs as input; • F-VGG (Simonyan and Zisserman, 2015), a VGG network pre-trained on the ImageNet dataset and fine tuned on the MoodShutter dataset; • F-ResNet , a ResNet pre-trained on the ImageNet dataset and fine tuned on the MoodShutter dataset; • F-DenseNet , a DenseNet pre-trained on the ImageNet dataset and fine tuned on the MoodShutter dataset; and • early fusion, a method which combines features learned by our methods before classification or regression tasks, and is very popularly used (Chen et al., 2017;Oramas et al., 2017).

the first is bi-modal retrieval, which utilizes one modality to find similar content in the other modality, such as finding the most relevant texts to a given image .


For acoustic content, DCRNN outperforms MFCC and convnet because it can learn acoustic information both locally and globally.

t att is an attention vector generated from T small with an average pooling layer, and works on every word vector of T large through the element-wise product.


In recent years, users have come to prefer presenting more attractive ingredients, such as image and audio in addition to natural words.

dCRNN consists of five phases, as illustrated in Fig.


These previous works cope with data in the early stage of social media, because users are limited to posting single-modal content.

an intuitive idea to gain a comprehensive learning of the entire meaning of social media is to integrate more modalities effectively.


A relation network (RN) is a neural network with a structure integrated for relational reasoning.

both of them are weak to capture the long-distance dependency information, facts prove that proposed relation layer succeeds in alleviating the deficiencies of the two by integrating CNNs with bidirectional RNNs subtly.


The vector sizes shown here are the ones suggested in the original paper.


We thank all the anonymous reviews who give a lot of valuable and insightful comments.

the first reason is that the dense embeddings in NMt are powerful representations.


The historical spellings in the test set which are the inputs of the model are also case-sensitive.

they hypothesize that the reason why the combination of these two models does not gain more improvement is that the multi-task learning has already learned the attention patterns.


Note that the same modern spelling may occur with different historical spellings.

there are only very few unchanged translations in the MT task.


Then, we use the probability scores of the sentence as features.

while the dissimilarity between the cluster enclosing Tunis and Sfax and the cluster containing the rest of the dialects is more than 50%.


Heuristically, we prefer shorter spans for entities, but guide ourselves with common sense.

we then classify the triples with a multi-layer perceptron with three hidden layers, 128 neurons each, with an initial learning rate of 0.01 that is divided by 5 if the validation score does not increase after two consecutive epochs by at least 0.001.


This is therefore a coarse abstraction of the structured prediction tasks presented in this paper.

so, though conceptually, the annotations by two people are similar, this is not captured by our calculation of inter-annotator agreement.


The same applies to experiencer: if the head of the governing phrase is an emotion, then the head of the current phrase is a potential experiencer.

experiencers, causes and targets are annotated in the whole sentence triple if they refer to an emotion in the target sentence.


For example, if we consider the previously mentioned example of say and said, the input sequence in this case would be sayε and the output sequence would be said.

the prediction problem would be much simpler if the insertion positions were in place, because the model would only need to decide which symbol goes in each position.


The hyperparameters for the spectral algorithm were tuned on the development set of the first fold, and then we used the hyperparameters identified at that phase for the rest of the folds.

our approach, like Koskenniemi's, is for now limited to character level tasks.


Up to this point, we trained our aligner.

target sentences are encoded like source sentences with an additional attention layer, which helps the encoder to recognize the most relevant features by emphasizing on critical points of the target sentence given each source sentence.


Similar to (dos Santos et al., 2014), our model is trained by contrasting positive and negative parallel sentences.

the computational results presented in this work have been achieved (in part) using the HPC infrastructure LEO of the University of Innsbruck.


As a consequence, these models are also time-specific.

in Section 2 we introduce the notion of 'semantic shift' and provide some linguistic background for it.


A famous example is the distributional model capturing the fact that the relation between 'man' and 'woman' is the same as between 'king' and 'queen' (by adding and subtracting the corresponding word vectors).

this vein of research lacks the cohesion, common terminology and shared practices of more established areas of natural language processing.


Text and Stucture Analysis: (4) LeadLDA (Li et al., 2016a) generates words according to topic dependencies derived from conversation trees.

given is the edges between vertices and T denotes text information of vertices.


They ignore that one discusses diverse topics when dynamically interacting with different people.

we emphasize on exploring whether user interactions and user attention can help topic generation in social media.


So amazing Louis Vuitton's necklace for Christmas gift.

is a user-user network from the target media.


TREE2SEQ and FOREST2SEQ, compared to the SEQ2SEQ model.

it is expected that modeling the syntactic structure should improve the performance of NMT, especially in low-resource or linguistically divergent scenarios, such as English-Farsi.


The RAML objective function is defined as follows: where Y is the set of possible model outputs.

to verify this goal, we investigate the entailment score for source-summary pairs for different models.


Our approach deals directly with raw sentence pairs to determine if two sentences are a translation of each other.

we observe that it becomes harder to identify  Table 1: Precision (P), recall (R) and F 1 scores where the decision threshold ρ maximizes the area under the precision-recall curve of the test sets with noise ratios of 0%, 50% and 90%.


In this study we did not consider out-of-vocabulary words.

we use the GIZA++ implementation (Och and Ney, 2003) 10 to train word alignment models in both directions.


However, we see that our neural network based approach obtains better performances by a significant margin.

we propose a bidirectional recurrent neural network based approach to extract parallel sentences from collections of multilingual texts.


The only difference is that Equation 1 is implemented with ITG RNN.

the probability of reordering is derived in the following way.


For GIC only, text lines were split on whitespaces and punctuation marks.

we added the symbols for the beginning and the end of words so each word is represented by 2 bigrams and several unigrams.


The first one encodes a word image into a vector and the second one decodes the vector into a character sequence or word.

iCR title lines were only split on whitespaces and we kept with the punctuations in the data.


Although many of the top-most publishers (say, the top 10) remain the same over long time spans, others make an appearance on lower ranks and are displaced again as time goes by and publishers compete for user attention.

when used as clickbait, these kinds of messages more often resemble spam, which is distinctly different from clickbait, but shares the goal of luring readers to their sender's web pages.


2 Developing human language technologies for low-resource languages has been an important challenge for years, and several studies attempted to bridge the resource gap across languages by cross-lingual   (Speer and Havasi, 2012) are also presented.

this characteristic boosted the performance of MtransE, which assumes one-to-one projection of concepts.


we conducted a grid search for learning rate α ∈ {0.5, 0.25, 0.125, 0.0625, 0.03125} and dimensionality d ∈ {8, 16, 32, 64, 128} (only for MLP).


Likewise, the MANUAL dataset had many concepts that were not in the training data for MTransE, which seriously degraded its performance.

conceptNet (Liu and Singh, 2004b;Speer et al., 2017) adopted natural language expressions such as words and phrases that may have ambiguities to represent knowledge, which made it possible to collect millions of commonsense facts in multiple languages via crowdsourcing.


• LIN: α = 0.125 for the English-Japanese dataset, and α = 0.5 for the English-Chinese dataset • MLP: α = 0.5 and d = 16 for the English-Japanese dataset, and α = 0.5 and d = 32 for the English-Chinese dataset We thank anonymous reviewers for their valuable suggestions.

(bat, Capa-bleOf, fly) results in 4 × 27 = 108 Japanese translation candidates in total, and even after filtering them Figure 1: Ambiguity of knowledge projection.


It is therefore unlikely to need a QE model that is capable of approximating non-stationary functions.

(Our training data are with around 10K-20K sentences.)


Second, most features in the feature space are useful.

this is because simple SE kernel function is not representative enough to learn the similarity in high-dimensional QE feature space.


Closely related to this is fine-tuning models on a new domain.

in the end, our large-scale QE feature space experiments are with two feature sets with 70 and 140 features respectively.


4 We first present results with WMT QE 2017 shared task (EN-DE and DE-EN).

we trained a multiplicative LSTM (Krause et al., 2017) with 4, 096 units on a very large public English corpus (see McAuley et al.


Race is, however, perhaps even more fraught with potential misunderstandings and abuses.

with previous research on race/ethnicity, we used labels obtained by directly surveying Twitter users, rather than distantly supervised geo-located data or perceived race labels, which lead to multiple biases and lower accuracies on real data.


In this paper, we study fake news detection with different degrees of fakeness by integrating multiple sources.

in the following subsection, we further investigate how each model component contributes to the performance improvement.


In this setting, we keep the automated feature extraction and the fakeness discrimination components.

these observations suggest that the proposed framework can effectively combine multiple sources.


In this setting, we use Center Loss and cross entropy.

it is crucial to classify fake news into multiple classes reflecting degrees of fakeness.


they attain F 1 = 0.88 using multi-sense embeddings with Multinomial Naive Bayes in a cross validation setting (vs. our F 1 = 0.81 on a test set).


• Twitter like any other social media is a live, thriving platform, taking a snapshot at one time and presuming it to work forever is not a sound judgment.

twitter's terms of service, under which sharing of aggregated resources (tweets) is barred, prove an unnecessary obstacle in this regard.


Most innovations do not succeed in becoming community norms, but some do.

(2017b), who introduce an inheritance graph to represent how speakers pass innovations (in this case called practices) on to others in time through real-life collaborations.


We consider strong-tie users those with a tie-strength value ≥ 0.5.

strong ties are usually those connecting family members or close friends, while weak ties connect acquaintances.


We now present four different methods for imputing the missing forms into the resulting partial paradigm.

these are, after all, more likely to agree other forms as well.


Therefore, we would expect most form patterns in the past perfect slot to look similar to x 1 +ed or x 1 +x 2 +n.

we can consider additional stem candidates, for example, ea and eat, which are substrings of the original stem.


We find that our approach performs particularly well on the task of named entity recognition.

al (2017) which proposed to extract hidden states from pre-trained word-level language models as features for downstream NLP tasks.


(2016) showed that concatenation of word embeddings learnt separately from a corpus and the WordNet to produce superior word embeddings.

gloVe: The global Vectors for word representation proposed by Pennington et al.


The evaluation measure is the ratio of the questions that were answered correctly in each dataset using a particular word embedding.

there is a slight drop in performance for the semantic similarity and psycholinguistic score prediction tasks when the dimensionality is reduced.


Autoencoders (Kingma and Welling, 2014;Vincent et al., 2008) have gained popularity as a method for learning feature representations from unlabelled data that can then be used for supervised learning tasks.

to sparse and high-dimensional counting-based distributional word representation methods that use co-occurring contexts of a word as its representation, dense and low-dimensional prediction-based distributed word representations (Pennington et al., 2014;Mikolov et al., 2013a;Huang et al., 2012;Collobert and Weston, 2008;Mnih and Hinton, 2009) have obtained impressive performances in numerous NLP tasks such as sentiment classification , and machine translation (Zou et al., 2013).


Although our model can pull sense vectors away from its reverse sense with the help of antonym and negative contextual information.

as this dataset does not provide context for disambiguation, we use the similar strategies from the semantic relatedness task: In avgSimD, we choose the first word iff avgSimD( 1 , ′ ) > avgSimD( 2 , ′ ) .


With the aid of recently proposed word embedding algorithms, the study of semantic similarity has progressed and advanced rapidly.

early approaches include utilizing the termdocument occurrence matrix from a large corpus and then perform dimension reduction techniques such as singular value decomposition (latent semantic analysis) (Bullinaria and Levy, 2007;Deerwester et al., 1990).


In natural language processing (NLP), recurrent neural networks (RNNs) are typically used as both the encoder and decoder, known as a sequence-to-sequence (Seq2Seq) model.

reference how many people were evacuated downstream ?


Since attention largely improves model performance for deterministic Seq2Seq models, it is tempting to include attention in the variational Seq2Seq as well.

we first implemented a traditional vanilla Seq2Seq model, which we call a deterministic encoder-decoder (DED), and generally replicated the results on the question generation task as reported in Du et al.


Both experiments will compare our new algorithm with their current best-performed models with state-of-theart results.

comparatively, during inference, only partial of test data with "unsatisfied performance" will be collected and further evaluated by DAT.


This paper introduces a framework and model to combine such diverse information from crowd workers, to measure and model individual annotator errors, and to form consensus final predictions.

existing crowdsourcing models cannot simultaneously model multiple kinds of information.


Corpora statistics and the results of evaluation are provided in Table 6.

to simplify the path for other researchers we release a script 3 , which installs necessary versions of the dependencies.


(9) only needs to be calculated for words in the batch vocabulary.

the shorter sequences are padded to have the same length as the longest sequence in the batch.


Each structural label is mapped to a fixed-length, trainable structural embedding.

we begin this section by describing the basic summarization framework, followed by our new copy mechanisms used to encourage source words and dependency relations to be preserved in the summary.


Future researchers could try to improve the diversity metrics while maintaining or improving the quality of the descriptions (ideally measured by human judgments).

we caution that the GAN-based systems are the only ones in our evaluation that are designed with diversity in mind.


Let D be a dataset that consists of triples including a posted question q, a headline candidate x, and a voted score y, i.e., (q, x, y) ∈ D. In the pairwise ranking method, we train a ranking model as a binary classifier that determines whether the condition y i > y j is true or false for two candidates x i and x j in the same question (q i = q j ).

we defined it with respect to the best candidate, i.e., precision@1, as follows: No.


Furthermore, recent sophisticated neural models for extractive summarization (Cheng and Lapata, 2016;Nallapati et al., 2017) basically require large-scale paired data (e.g., article-headline) to automatically label candidates, as manual annotation is very costly.

we further randomly divided the uninformative group into two equal groups: changed and unchanged.


Unlike the above tasks, SCT (Mostafazadeh et al., 2016) provides large-scale supervised training stories of temporal and causal relations, ensuring a high-quality evaluation for common sense knowledge understanding of mechanisms.

we combine the plot vectors and ending representations to create a word-by-word interaction attention tensor, in which each channel represents the interaction of the word in one dimension.


Research for these two tasks are fueled by several manually generated corpora (Lin et al., 2014;Zhu et al., 2016).

we use fc7 feature extracted from VGGNet to represent a given image.


We take two attributes of natural questions into consideration while training the generator.


Our approach is informed by methods in cognitive neuroscience and psycholinguistics, where such controls are standard practice for studying the brain.

as described above, this system takes input constraints targeting semantic, syntactic, and lexical components, and produces diverse, meaning-annotated sentences meeting those constraints.


Once these models are trained, they are used to produce the 4000 sentence embeddings that will serve as training input to the classifier (and the 1000 sentence embeddings used for testing).

for these tasks-as long as vocabulary words are not out-ofvocabulary for the models (which we confirm below)-the important thing should be not what the words themselves are, but whether dependencies between them have been captured in the sentence embeddings.


Recently, researchers have started to work on morphologically informed word embeddings (Botha and Blunsom, 2014;Cao and Rei, 2016), aiming at better capturing syntactic, lexical and morphological information.

different from recent work employing complex attention mechanisms Cui et al., 2017a;Sordoni et al., 2016), our attention mechanism is much more simple with comparable performance so that we can focus on the effectiveness of SAW strategies.


Table 2 shows the results of different models on the MultiNLI dataset.

h ∈ R T ×2d and A ∈ R T ×2d are the hidden vectors at the top layer and weight matrices, respectively.


Once, a preposition and its corresponding sense are known, the space of verb+preposition constructions that can be used for paraphrasing is severely reduced (illustrated in Table 1), thereby making the task of verb+preposition paraphrasing easy.

it shows that NC-LSTM outperforms Girju (2007)'s approach on their own dataset.


Such errors add noise to the training dataset.

also, NC-LSTM easily outperforms NC-FFN by a significant margin when the network parameters are further tuned with a portion of the dataset.


"No," the doctor's young and pretty wife whispered in reply.

this further highlights the limitations of Word2Vec similarity described in Section 2.1 as our strength predictor is clearly learning something that cannot be captured by cosine similarity alone.


Early humour generation systems tended to model jokes' setup/punchline structure explicitly.

measuring incongruity and overlap in terms of similarity is a rather odd choice.


On the basis of the annotation, we also determine the most probable stance labels according to MACE (Hovy et al., 2013), and compare them to the ground truth from the Emergent project.

the systems fail when semantic relations between words need to be taken into account, complex negation instances are encountered, or the understanding of propositional content in general is required.


Mihalcea and Strapparava (2005) reported that using contentbased features could achieve big improvements.

2) Humorous texts are more vivid and specific.


The other (N M N ) is the normalization of the length of the modified structures.

the features that have consistent negative correlations are the length ratio (LR NP) and the average length of noun phrases (AP L 2 NP).


We define a random lookup table for these features, and learn its parameters during training.

our feature set is crafted from distant supervision applied to Wikipedia, a much less time-consuming process which we showed to be nevertheless adapted to rare words.


A naive Bayes classifier is then trained by the EM algorithm in a semi-supervised manner.

manually collecting a small number of labeled documents remains expensive in many text analysis applications.


(2016) the outputs are chosen from the same top 1,000 frequently occurring answers, the coverage of this set for their VQA corpus is 86.54%, unlike our 66.7%.

striking features of an image are usually picked up by several questions.


As long as this run-on-line also occurs between each singular colon of the poem, i.e.

finally, der minister bedauert derartigeäusserungen der minister bedauert derartigeäusserungen der minister bedauert derartigeäusserungen der minister bedauert derartigeäusserungen der minister bedauert derartigeäusserung n der minister bedauert derartigeäusser ng n der minister bedauert derartigeäuss r ng n der minister bedauert derartige ss r ng n der minister bedauert derartig ss r ng n der minister bedauert derart g ss r ng n der minister bedauert der rt g ss r ng n der minister bedauert d r rt g ss r ng n der minister bedau rt d r rt g ss r ng n der minister bed rt d r rt g ss r ng n der the (c) decomposition styles can already be differentiated based on textual information.


Our model so far is still far from optimal (beyond the fact that we have not searched meta-parameters that yield best classification results) and we want to point out some aspects of future work: enjambments are linked to syntactic characteristics and we could integrate syntactic information (such as part-of-speech (POS) tags) or try to pre-train our character encoding to decode the POS tag sequence.

while we have provided external evidence that the classifier does differentiate the fluency classes with the confusion matrix presented in Section 5, we wonder if the internal poem representation also reflects the ordering in this continuum.


Most question answering (QA) systems are based on raw text and structured knowledge graph.

raw text corpora are hard to understand for machine.


Output of cell-LSTM can be used by the following row and column encoders as their input.

there is no such case in both the training and testing set, we did not explore the way to measure the threshold.


How to collect a large number of question pairs in variant linguistic forms, which may present the same intent, is the key point for such corpus construction.

in LCQMC, the non-matching pairs satisfy the requirement very well, because the non-matching pairs are selected by WMD and only kept with smaller Wassertein distance.


The more similar the pair is, the smaller the distance value between two sentences is.

the main difference is that these researches focused on the judgment of relevance between questions or between the candidate questions and queries instead of semantic equivalence.


The increase in F1 score was highest for MUC 6 (2%-5%) and for re3d (2-3%).

this is often done manually (Daumé III, 2007;Augenstein et al., 2017), though Kim et al.


LNEx provides the foundation for localizing information, and with increased availability of open data, we expect our approach based on region-specific knowledge to be widely applicable in practice.

in the future, we might consider extracting them at a reduced cost by integrating LNEx with an incremental learning method (Al-Olimat et al., 2018).


We trained both tools interchangeably on our annotated datasets in addition to all the data from W-NUT '16 13 while retaining the annotations and unifying the classes we consider as locations (i.e., geo-loc, company, facility) into one type.

pragmatic influences on writing style shorten names to reduce redundant content in social media.


Two popular datasets, SimpleQuestions (Bordes et al., 2015) and WebQuestions (Berant et al., 2013) are used in our experiments.

for a given KB K, we will denote its set of entities by E and its set of relations by R. The KB K contains a set of factual triples f in which each triple is in the form of (s, r, o), with s, o ∈ E and r ∈ R. for example, (CarlosGomez, baseball.baseballPlayer.position, Centerfielder) is one such triple taken from freebase.


On WQ, a given question q may have multiple answer paths (s, r, o) in the ground-truth answer set A(q).

this demonstrates the effectiveness of augmenting a model with a verification mechanism.


KSE is an separately pre-trained model to embed the relations and types in KB in an Euclidean space, by using an adaptation of the TransH (Wang et al., 2014) to the "schema graph", which is a graphical representation of the KB schema set S of all "schema triples" (t, r, t ) ∈ T × R × T for which knowledge triples (s, r, o) with t(s) = t and t(o) = t are allowed.

it is worth noting that to properly define the verification loss ve , we need to introduce "negative examples" for CLS.To that end, denote D qsr is the set of all training examples for path label prediction (ENC-DEC).


The parameters are updated with a multi-task training schema.

reading comprehension makes reasoning over documents instead of parsing the questions.


Based on the experiment results, we make several observations: (1) Our method performs better than the other methods, and are proved to be highly reliable for the text classification task.

for further investigation, we compared the classification performance of these two word embeddings in each category.


This is mainly attributed to the task-specific modeling mechanism, which enables our models to capture functional features among words, therefore, it can more accurately distinguish classes.

in real-world applications, such as text classification and information retrieval, word contexts alone are insufficient to achieve success in the absence of task-specific features.


Section 3 details the proposed SNN and two fusion methods.

the accuracies are MR (82.1%), SST-5 (50.4%), TREC (96%) and SUBJ (93.9%).


As shown in Table 6: g st denotes structure weight and att st denotes structure attention as mentioned in Section 3.

it proves that our attention mechanism is very reasonable.


But recently more works prefer to share deeper layers to share more highlevel knowledge, which has been proven effective to enhance the performance (Liu et al., 2015;Liu et al., 2016).

memory mechanism is employed in this unit to control the feature sharing.


Specifically, the precision on the subset of sentences matching q strict is 0.42, a factor of two compared to the precision of (Levy et al., 2017) on this set of sentences.

the rest of the labeling process was done similarly to (Levy et al., 2017).


Many models targeting NLP tasks are designed to work independently although they usually employ linguistic information related with other tasks.

the root and the morpheme sequence are used to generate a representation as depicted in Figure 1.


As one can see from the table, it achieved the best results compared to our joint models.

we follow a conditional random field (CRF) based approach to model the dependencies between the consequent tokens (Lafferty et al., 2001).


(McCallum and Li, 2003;Finkel et al., 2005;Humphreys et al., 1998;Appelt et al., 1995).

we argue that our approach is immune to this problem as all of these output formats can be treated as a sequence.


Although this mapping process is conceptually straightforward, there are a number of nuances and complexities we had to overcome with a combination of special-case handling and heuristics.

consider the question "Which city is Mccormick Field in?"


(2017) mapped the dataset from Freebase to Wikidata.

the same process can be applied to cases where we have successfully mapped the entities but not the predicates, and even to cases where we have only successfully mapped the topic entity.


(2012), Volkova and Bachrach (2016) make use of Twitter distantly labeled data.

tEC the twitter Emotion Corpus published by Mohammad (2012) is built on social media.


They achieved 88.3% F score on the English CoNLL 2003 data.

each character was encoded in bytes and BTS achieved high performance on CoNLL 2002 and 2003 dataset without any feature engineering.


To increase the coverage, we add an additional names to the list.

for example in figure 3, except that "皮鞋 (leather shoes)" have definite labels, all of the remaining characters can be labeled as "B-PDT", "I-PDT" and so on.


Our selector can be trained along the guidance of these prior knowledge about which sentences are labeled correctly.

we use Adam (Kingma and Ba, 2014) to train the instance selector with the learning rate 0.001.


Many previous works (Lample et al., 2016;Peng and Dredze, 2015b) have shown that pre-trained embeddings on large-scale unlabeled corpus enable to initialize the table and observe efficiently improvements.

in practice we find that the automatically labeled NER data suffers from two problems, i.e., incomplete annotation and noisy annotation, which negatively affect the performance of NER systems.


We use different alias mappings for TAC and CoNLL.

it is interesting to note that our local model is solid, but is noticeably inferior to the stat-of-the-art local model.


The p(e|m) is also transformed to a 10-d vector using RBF binning (Sil et al., 2018) to create prior feature f (p) ij ∈ R 10 .

for each mention x i we use ground truth entities for the other mentions in E i and update weights of C and w such that the true candidate of x i gets higher score than its false candidates.


This suggests that our progressive learning technique enables the models to more accurately predict the new categories and in a shorter time.

even if we increase the training epochs to 60 for the model using randomly initialized parameters, transferred parameters still perform better.


From the QA collections, we can obtain question-answer pairs.

we utilize the Positional Language Model (PLM) (Lv and Zhai, 2009) for question retrieval and obtain a ranked list of retrieved existing questions denoted by L q * ,a * |q .


Especially when a multitude of reviews of a specific product are gathered, the different experiences enable the consumer to obtain a well-informed conception of the qualities and deficiencies of the respective article.

these models have the advantage that they generally require little feature engineering.


Without manual labels for all bigrams, another option for roughly checking the quality of the bigram matches is to check if the sentiment of the bigram is consistent with that of the sentence.

the precision achieved by the various classes seems very promising, in particular considering that the only sentiment-annotated input for learning these classes was a unigram sentiment lexicon.


Then the word preventing from the class REV⊕ will be matched (line 1 in Table 1), since it is followed by a negative unigram.

the case is similar for negative F 1 and accuracy, which improve up to DOM.


We use precision, recall, and F 1 to measure performance for positive and negative classes and accuracy and coverage to measure overall performance, following previous work on this dataset.

we found that it is beneficial to also take into account the uncertainty stemming from the automatic sentiment prediction of the unigrams and bigrams.


We convert the scores to classes and report our results on both the regression and classification tasks.

in the tertiary task, the respective scores are 0.54 / 0.91 / 0.68 (FALSE) and 0.91 / 0.85 / 0.88 (TRUE).


Our error analysis leads to a discussion reported in Section 6 and plans for future work.

have used the CoNLL-2011 Shared Task distribution of OntoNotes 1 (Pradhan et al., 2011).


This contradicts the finding of , who found that considering all features yields the best performance.

(2015) argue that contextual discourse information plays a critical role in negation focus identification and propose a word-topic graph model that uses this contextual information from both lexical and topical perspectives.


For example, Sentence 7 would result in the interpretations 7a and 7b with this procedure.

in the end a binary classification system that can predict whether some positive meaning is implied in a negated statement or not would suffice in most real-world applications.


Classical architectures include convolutional neural networks (CNNs) (Zeng et al., 2014), recurrent neural networks (RNNs) (Xu et al., 2015), etc.

in the network, the convolutional and recurrent network units are employed to model local information such as syntactic and lexical features from the sentence level.


Therefore, considering the sparsity of domain knowledge, these methods applied on such corpus will introduce plenty of small classes, which are in fact noises and thus meaningless.

, Cm) 8: if r * / ∈ R old then 10: Label all entity pairs in C * with relation r * to be Y * 13: if NotNearUniform(Pr(r|e1, e2, N Nt)) then 18: Label (e1, e2) with relation argmax r * Pr(r|e1, e2, N Nt) to be Y * 19: end if 21: end for 22: In each iteration t, the first step is to train base neural network N N t with training data X l and Y l .


After the execution of the above three modules in one iteration, if a new relation is found with its seed relation instances (i.e., entity pairs), the structure of the neural network retrained in the next iteration will be adjusted dynamically with parameters updated.

we present a syntactic unit as the concatenation of four parts: where dir are the embeddings of the word, POS tag, dependency relation and relational direction, respectively.


Distant supervision is another element of IE that is employed in this research to generate training data for police killing detection.

distant supervision is another element of IE that is employed in this research to generate training data for police killing detection.


State-of-the-art Open IE systems (see Section 2) identify relationships between entities in a sentence by matching patterns over either shallow syntactic features in terms of part-of-speech (POS) tags and noun phrase (NP) chunks or dependency tree structures.

(2015) by matching an extraction with a gold proposition if both agree on the grammatical head of both the relational phrase and its arguments.


As mentioned, MT and LN use one-shot learning whereas Emboot is iterative and, thus, more prone to semantic drift.

to the best of our knowledge, we report the latest state-of-the-art results on the semi-supervised named entity classification task.


The recent success of deep learning for NLP tasks is at least partially due to the fact that such models are exposed to large quantities of labeled data.

with MT, we can "plug in" any task simply by generating a different input vector X (and, potentially, a different scheme to insert noise), whereas LN requires an adaptation of the entire architecture due to the tight connections between teacher and student.


Then the algorithm iteratively applies the following three steps for a specified number of epochs: (1) Learning custom embeddings: The algorithm learns custom embeddings for all entities and patterns observed in the dataset, using the current entPool c s. We train our embeddings for both entities and patterns by maximizing the objective function J: where the individual components of the objective function designed to model both the unsupervised, language model part of the task as well as the light supervision coming from the seed examples.

it is important for unlabeled data to be used for regularization to be able to learn good abstractions in the different layers.


We selected the ReLU activation function except for the attention layers.

the modality fusion outperforms both shallow fusion (directly using SVM classifier) and deep fusion (DF) on diverse feature extraction combinations.


The results show that the proposed network improves performance on noisy data.

the proposed hybrid attention architecture helps the system focus on learning informative representations for both modality-specific feature extraction and model fusion.


To make the system learn this difference, we proposed a modality attention fusion that puts an attention Figure 4: Modality Fusion layer over the extracted modality-specific features, helping the system focus on the informative modality.

it is hard for a computer to precisely interpret human meaning because: 1.


3, we can see that K is based on f (w) 2 , i.e., the second power of each word frequency.

one should always keep in mind the fact that its difference is (at least superficially) large when one uses it.


Most of these works model the sequence of words to learn document representation.

• The argument sentence is extracted from each document.


Grammatical error correction (GEC) tools can help language learners revise their writing.

dEV is the POS tag of Chinese adverb marker "地".


Distributional representations of concepts are often easy to obtain from unstructured data sets, but they tend to provide only a blurry picture of the relationships that exist between concepts.

frameNet contains two distinct entity classes: frames and lexical units, where a frame is a meaning and a lexical unit is a single meaning for a word.


We would like to thank Adam Foster, Ben Peloquin, JJ Plecs, and Will Hamilton for insightful comments, and anonymous reviewers for constructive criticism.


The labels correspond to the actual stops in the utterance.

to solve this problem, we look at sub-word units in Mongolian word, and encode their information to a meaningful representation, then fed it to LStM to decode the best corresponding PB label.


That is, we choose t 1 for a given s 1 , s 2 as follows: where S b ∈ S is the current mini-batch.

the training data consists of a set S of phrase or sentence pairs s 1 , s 2 from either PPDB (Ganitkevitch et al., 2013) or the aligned Wikipedia sentences (Coster and Kauchak, 2011) where s 1 and s 2 are assumed to be paraphrases.


Among diverse methods for calculating word embeddings, we empirically show the uncorrelation in word embedding dimensions, which is one of the prerequisites for simplifying the bilinear operator to a linear one.

the second expectation term in the right hand side of (18) can be computed as follows: When i = j, each of the four expectations in the RHS of (21) are zero from the uncorrelation assumption.


This is a linear transformation in the word embedding space and does not affect the topology of the embedding space.

relational Independence Word pairs in the training data are assumed to be i.i.d.


Expected correlation of different dimensions in the same word is zero from (8).

if we assume that word embeddings are standardised, uncorrelated and word-pairs are i.i.d, then we prove in §3 that bilinear relational compositional operators are independent of bilinear pairwise interactions between the two input word embeddings.


In the first approach, from given corpora or knowledge bases, word and relation embeddings are jointly learnt such that some objective is optimised (Guo et al., 2016;Yang et al., 2015;Nickel et al., 2016;Bordes et al., 2013;Rocktäschel et al., 2016;Minervini et al., 2017;Trouillon et al., 2016).

first and fourth expectations in (11) involve the same pair of words.


We showed that, if the word embeddings are standardised and uncorrelated, then the expected 2 distance between analogous and non-analogous word-pairs is independent of bilinear terms, and the relation embedding further simplifies to the popular PairDiff operator under regularised settings.

a does not play any part in the expected loss over positive examples.


First, we construct a mapping of the tags of the UoI corpus to the Universal Part-of-Speech tagset (Petrov et al., 2012).

language documentation neither is nor needs to be restricted to such minimal amounts of annotation work.


For instance, in E2, the sentence "It has 4G RAM" and "But you must use it carefully" are uncorrelated with all three sub-questions.

generally, the two categories, "Matching" and "Non-matching" has following cases.


A highway network is an architecture that allows a model to train a very deep network efficiently (Srivastava et al., 2015).

this indicates that considering subword information is effective at representing words and useful with many languages.


The proposed model showed better estimations of rare and complex words.

some works used word and subword representations concurrently to produce better word representations.


Distributed representations of words play a major role in the field of natural language processing by encoding semantic and syntactic information of words.

nearest words are sorted in descending order of similarity that computed by cosine similarity.


The input is a concatenated sequence of characters and the output is a sequence of words with white space (•) as word boundary marker and ZWNJ (T) as subword boundary marker.

section 4 briefly discusses challenges in Urdu word segmentation.


Statistical methods like n-grams have also been extensively used to segment character sequences into words and have proven very effective (Kawtrakul, 1997;Aroonmanakun, 2002).

in the absence of word boundary markers, tokenization of text for further processing is a nontrivial and challenging task.


The synonyms were proposed randomly (in terms of difficulty) and were not contextualized.

the training set based on this technique is called Manulex-Cont.


Yet, they have been automatically built from multilingual networks and therefore the granularity of their senses often lacks precision to be exploited for our purposes (not to mention wrong translations in the case of the French version of BabelNet).

it turns out that not all synonyms for a given word are grouped together in distinct senses.


1 Such systems face three main challenges: variability, ambiguity and discontinuity (Constant et al., 2017).

thirdly, some types of syntactic dependencies can be specific to some VMWEs, e.g.


A robust multilingual topic model should produce similar distributions over topics for a pair of word translations c = (w 1 , w 2 ), where we call c a concept.

the threshold is defined with respect to the maximum value of δ.


Nonetheless, the noted grammatical similarities among these languages are expected to be sufficient for the generalizability of the pluralization approach.

in chiShona for example, gumbo 'leg' is pluralized as makumbo instead of the generated magumbo.


We also consider two polarity detection datasets (positive or negative), namely the Polarity04 (pol.04) (Pang and Lee, 2004) and Polarity05 (pol.05) (Pang and Lee, 2005) datasets, and finally a 10k document subset of the apps for android (apps4and.)

semantic Vector Networks may be thought of as a natural way of enriching word-level semantic representations, which may in turn be useful for informing a neural architecture with relational (e.g., commonsense or lexical) knowledge.


We evaluated the models on the relatedness subtask, which is constructed in a similar manner as STS benchmark.

distributed models like sif and inferSent improved the correlation of A for SICK, and both A and C for STS Benchmark.


Second, the system searches a dictionary for candidate expressions in the category corresponding to the presumed intention.

fEs have a communicative function and thus in fE dictionaries, they are classified into categories on the basis of their functions.


Overall, in our research, we defined FEs as word sequences whose functions correspond to one category.

finally, in this annotated dictionary, each categorised fE has some example sentences derived from an actual corpus (figure 3).


Table 2 shows the number of FEs and sentences after the paraphrasing.

we first consider a semantic searching system that searches for FEs.


Finally, the natural language inference task is represented with the SNLI dataset (Bowman et al., 2015).

the two hidden representations are merged and fed into a 50-unit dense layer, over which 3-class classification with softmax is performed.


There are multiple proposals for "subconscious intrinsic evaluation" (Bakarov, 2018) based on correlations with psycholinguistic data such as N400 effect (Van Petten, 2014;, fMRI scans (Devereux et al., 2010;Søgaard, 2016), eye-tracking (Klerke et al., 2015;Søgaard, 2016), and semantic priming data (Lund et al., 1995;Lund and Burgess, 1996;Jones et al., 2006;Lapesa and Evert, 2013;Ettinger and Linzen, 2016;Auguste et al., 2017).

as for the extrinsic tasks, the immediate observation is the low correlation between all the intrinsic and 3 sequence labeling tasks.


Since linguistic relations (especially semantic relations such as hypernymy and antonymy) cannot yet be classified accurately by purely distributional means 2 , LDT relies on the largest freely available lexicographic resources: WordNet (Fellbaum, 1998) and Wiktionary 3 , with the option of BabelNet (Navigli and Ponzetto, 2012).

these studies have a limited selection of word embeddings (amounting to 9 and 5 data points, correspondingly).


A node indicates the sense of a word and an edge denotes the semantic relationship between the senses.

babelNet 2 is a multi-lingual lexicalized semantic network and ontology.


However, constructing training corpus for all languages and words is tremendously expensive, so the supervised approaches generally have some limitations on the set of the words that can be disambiguated.

in this case, we can map 'airline' to 'airline Noun#2', 'hires' to 'hire Verb#1,' 'new' to 'new Adjective#6,' 'cabin' to 'cabin Noun#3' and 'crews' to 'crew Noun #7' by the context of this sentence.


I-measure, on the other hand, assigns a negative score for Hypothesis 2 as it is considered to 'degrade' the input, although it is arguable that both hypotheses 1 and 2 are equally ungrammatical.

spearman correlation is more relaxed compared to Pearson correlation in terms of the assumptions made about variables and also less sensitive to outliers in the sample.


The coefficients c ij is computed using a softmax function, and M j=1 c ij = 1.

the major difference between DR-AGG and self-attention (Lin et al., 2017;Yang et al., 2016) is that the query vector of self-attention is task dependent trainable parameters learned during the training phase, while the query vector of DR-AGG is each input or output capsule which is instance dependent and dynamically updated.


In SRL task, the distribution of the role labels is similarly uneven and the problem comes worse after we introduce the additional <VR> node and None label, directly applying the primitive form of bilinear attention would fail to capture the prior probability P (y i = c k ) for each class.

semantic role labeling (sRL) is a shallow semantic parsing, which is dedicated to identifying the semantic arguments of a predicate and labeling them with their semantic roles.


The parameters in our model are optimized with Adam (Kingma and Ba, 2015), which keeps a moving average of the L2 norm of the gradient for each parameter throughout training and divides the gradient for each parameter by this moving average, ensuring that the magnitude of the gradients will on average be close to one.

the k-order pruning does not boost the performance of our model.


Compared to syntax-agnostic models, our model overwhelmingly outperforms (with an improvement of 0.9% F 1 ) the previous work (He et al., 2018).

we use the automatically predicted parse with moderate performance provided by CoNLL-2009 shared task, with the LAS score about 86%.


The first kind is the closed author set problem where the test-document is written by someone within the set of candidate authors being examined.

the work in Eder (2013) studied the effectiveness of POS trigrams with a set of 21 authors and reported an accuracy of 65%.


Instead of including thousands of feature values into an author template, we find the most frequent and then within them the most informative feature values of each feature type.

(2011), we use them in building author templates, and combine the information of these features with other syntactic feature types in a completely different and novel way.


Details of the preprocessing procedures and the finally determined model parameters are given in Appendix A.

we did not utilize any external information, such as a named entity database or knowledge base with entities like city, airport, person, etc., to focus on the performance only using the lexical input of language.


the bold and underlined letters in samples are targets for analysis.


The Hybrid RNN 2 Attention BiRNN : https://github.com/HadoopIt/rnn-nlu and Hybrid RNN : https://github.com/mesnilgr/is13  (Mesnil et al., 2015) applied the RNN to slot filling for the first time, the Encoder-labeler Deep LSTM (Kurata et al., 2016) applied the encoder-decoder concept to slot filling, and the Attention BiRNN (Liu and Lane, 2016) applied attention and multi-task learning to slot filling and intent detection and achieved the state-of-the-art performance among the previous approaches for both.

we did not utilize any kind of pre-training or external data for any of the experiments.


In practice, we constitute the function in (12) used for computing weights by fully connected neural networks.

there have been various attempts to improve the performance of RNNs for spoken dialogue systems such as label sampling (Liu and Lane, 2015), the hybrid type of Elman and Jordan (Mesnil et al,.


These extracted components represent the foundation for the generation of a process model from a text.

table 1 provides an excerpt of an event log for the bicycle manufacturing process used in this paper.


Non-compliance represents a risk for many organizations.

it is not explicit in the textual description what to do if the result of the check is negative.


Our deep neural network succeeds in extracting the text subtleties essential for this complex task.

our RDV-CNN could capture only one premise for a target sentence and map them.


The single-domain and cross-domain attribution performances are reported in Tables 1 and 2 respectively.

• no NNP: This utilizes posts in which all proper nouns (NNP, NNPS) are replaced by <T>.


To keep the approach purely syntactic, we remove the leaf nodes which contain the sentence words (Fuller et al., 2014;Björklund and Zechner, 2017).

we use vertical and horizontal Markovization order of two (h=2, v=2) since higher orders did not seem to improve performance.


For common nouns, verbs, adjectives and adverbs, masking off certain topic words yield better performance suggesting that the remaining words corresponding to these lexical POS may help represent style.

the chosen data subset contains 160,482 posts across 674 users.


This demonstrates the importance of supervisions in capturing the deep semantic meaning of documents for our prediction task.

news Figure 1: Event "Salesforce rules out Twitter bid" showing different impacts to differnt firms.


Based on Theorem 1, under the conditional independence assumption, Corr(P (Y = 1|X), P (C = 1|X)) 2 equals 1, so that Corr(P (Y = 1|X), P (Y = 1|P (C = 1|X))) 2 equals 1 as well.

in the following sections, when we aim to investigate the impact of a single context feature C k or a single task Y t , for simplicity, we use a random variable C or Y to denote C k or Y t respectively.


The underlying intuition known as distributional hypothesis (Harris, 1954) can be summarized as: "a word is characterized by the company it keeps" (Firth, 1957).

corr (P (c = 1, Y = 1) − P (c = 1)P (Y = 1)) 2 P (Y = 1)(P (Y = 0))V ar(P (c = 1|X)) In the equation, the term (P (c = 1, Y = 1) − P (c = 1)P (Y = 1)) 2 describes the dependency between context feature c and the class label Y .


Based on the theory about the function of discrete random variables, we analyze the impact of context features and the functions that convert co-occurrence measures to prediction scores.

in practice it is almost impossible to get the case with exact conditional independence in different tasks.


Again, it shows that the two enhancements are not mutually exclusive with external data.

again we can see the same tendency when combining WMI.


in this work, we explore the problem of creating tags for movies from plot synopses.


In general, an FFNN consists of an input layer with activation a (0) := emo s (w i ) ∈ R |s| followed by multiple hidden layers with activation a (l+1) := σ(W (l+1) a (l) + b (l+1) ) where W (l+1) and b (l+1) are the weights and biases for layer l + 1 and σ is a nonlinear activation function.

as can be seen, regarding VaD, Valence is by far the most important dimension with a performance drop of .12 when ablating it.


An illustration of VAD and its relationship to Basic Emotions is given in Figure 1.

to WEI, ERM is based on the condition that the pairs of data sets in Table 1 are complementary in the sense that, when combining these lexicons, a subset of their entries are then encoded in both emotion formats, i.e., VA(D) and BE5.


Combined with lexicon-based features and transfer learning, our model achieves stateof-the-art performance, outperforming the previous best system by 4.4% Pearson correlation on the WASSA'17 EmoInt shared task dataset.

note that the pairwise correlations (heuristic 2) is only a guiding principle -the multi-task model discovers the optimal branching or sharing of layers based on the performance across a lot of variations.


In the subordinate clause, this feature is specified on the MOD list of the adposition subordinator, or the lexical type of the adverb subordinator.

first, we expected that adverb subordinators would be high-attaching, based on a cross linguistic generalization about adverb classes and the absence of any attested low-attaching adverbs in our literature review.


However, the Grammar Matrix provides a particularly suitable framework for our analyses because it requires its libraries to be typologically robust.

2001), we cannot introduce the subordinate predication on the adverb.


HPSG uses lexical entries, lexical rules and phrase structure rules, encoded as feature structures, to model language.

with the adposition subordinator, the adverb subordinator is not a syntactic head, and therefore has more flexibility with respect to its position in the subordinate clause.


Different SRNNs have achieved the best performance on different datasets.

the function f could be a nonlinear activation function such as sigmoid, or a linear activation function such as rectified linear units (Le et al., 2015).


To answer the questions correctly, we try to locate the paragraph which mentions the specific information, and then find sentences and words in this paragraph that can answer the question.

cNNs may not obtain the order information of the sequences, which is very important in NLP tasks.


For instance, one plausible intuition is that syntactic and semantic tasks might benefit among their two separate groups though cross-group assistance is weak or unlikely.

understanding the nature of the relationships between them is likely to have a broad impact on many downstream applications.


One factor that may play a role is the training set sizes of these tasks.

in addition to pairwise MTL results, we report the performances in the All and Oracle MTL settings in the last two rows of Table 3-5.


We implement our models in PyTorch (Paszke et al., 2017) on top of the AllenNLP library (Gardner et al., 2018).

sEM is harmed more than it is benefited but pairwise MTL performances cannot tell.


Each of 6, 248 sentences are annotated with a target element and the task is to predict the sentiment regarding that element (positive, negative or neutral).

for our final task we consider a much more sophisticated model using LSTMs (with Python's KERAS 3 ) to perform target-dependent sentiment classification (Tang et al., 2016) using twitter-specific sentiment word vectors  on the benchmark twitter dataset collected by Dong et al (2014).


The combination of these competing variabilities makes up the total internal variance which, as confirmed by Figure 1, is nevertheless smaller than with single train-test splits.

this variability means that our tuning procedure is often failing to choose the parameter that gives the best global performance and so produces sub-optimal models.


K-fold cross validation (CV) is a popular method for estimating the true performance of machine learning models, allowing model selection and parameter tuning.

we will see that the optimal choice of K is problem-specific so there is no guarantee that Kohavi's studies are comparable to modern NLP tasks.


Figure 2(a) shows that partitioning can have a large effect on the chosen parameter, leading to choices of max f eatures across the wide range of 0.03 to 0.4 (almost covering the whole search space).

"true performance", consists of the global score on the held-out reviews.


These results showed that partial sentence structures grounded in sets of nouns are useful for learning in natural corpora; the model's constrained distributional learning component offers one account of where these partial sentence structures might come from during early acquisition.

annotators were instructed to try to use the previous senses as much as possible.


• We demonstrate the effectiveness of the approach on top of a strong system (Transformer) which has already used residual networks.

the attention model used in transformer is scaled dot-product attention 1 .


Due to the significant different word formation, the Chinese loanword detection achieves the worst results.

compcorp4Train:We used these data sets to train word embedding models, and apply them in crosslingual word embedding based loanword candidate list generation approach.


When borrowing, a word in the donor language may adopt the receipt languages pronunciation system.

let W = {w 1 , w 2 , ..., w |w| } be the vocabulary of a language lang A with |W | words, and W ∈ R |W |×l be the corresponding word embeddings of length l. q firstly.


In this paper, we present an approach to regulate the information flow in neural machine translation model explicitly.

the study in  suggests that different target words require inconsistent contributions from source context (c t ) and target context (i.e., y t−1 and s t ).


We choose IRTGs because the separation of the derivational process in an RTG from the interpretation in algebras implies that (i) only one algorithm needs to be formulated to capture a large class of grammar formalisms and that (ii) the nonterminals cannot be observed in the generated structures.

hence, an efficient way to obtain P (a | G, p) is computing β(S Ga ).


One reason might be error propagation due to wrong POS tags predicted by the external tagger that we use.

instead we construct (a grammar that is isomorphic to) G f a via the grammar morphisms ϕ a and µ such that the diagram on the right commutes.


at least with the used version of EM training, the initial granularity of the nonterminal set has a decisive impact on the quality of the resulting grammar and should be chosen carefully.


In the LSTM based model, the tokens are encoded/decoded using LSTM units, which can effectively summarize the temporal information of the sentences in source/target domains.

we also list some previous works for comparison.


These observations have revealed that the gold POS tags are important for solving VP and NP attachment errors.

the parsing results (F 1 ) on the test set of the Vietnamese treebank are presented in Figure 1.


While section 2 reviews two widely used training criteria for un-normalized model, Importance Sampling and NCE, section 3 provides an empirical analysis of the NCE.

the main noticeable result is that learning Z c as a parameter is not working well when we have less data -in this case, initializing the output bias to − log |V| is far more efficient, while initializing output bias with values of log P n (w) gives even better results.


Let us explore the repartition of the partition function values during the last epoch of training.

since values of P n are always smaller than 1 and k = 100, and knowing the partition function at the beginning of training is large for all contexts, we can assume that r θ (ŵ i |H) will be large for a least part of the set of noise samples (ŵ i ) k i=1 .


Therefore the model will be able to learn first from words w with the higher values of P n (w).

to understand why, consider the weight of the gradient of the data-dependent term: .


In this paper, we analyse and explain some of the weaknesses of this objective function, linked to the mechanism of self-normalization, by closely monitoring comparative experiments.

we also explored several remedies and showed that when including the scaling parameter in the parameters of the model, the scaling process can be both stable and efficient.


The writing system is syllabry where each character represents a consonant and vowel.

for instance, even though Afan-Oromo follows SOV word order format, nouns can be changed based on their role in a sentence which makes the word order to be flexible.


The development of machine translation more often uses statistical approach because it requires very limited computational linguistic resources compared to the rule-based approach.

as there were no such corpora for SMT experiments, the researchers had to prepare small size corpora.


The script is used to write other languages like Amharic, Tigrigna, Argoba, etc.

there are some prepositions written as a separate word.


find new parallel sentence pairs x : z from existing data x : y and y : z, with common y) or translate by bridging (translate from x to y, and then from y to z) for the zero-shot pairs.

our experiments on the Europarl corpus show that multilingual translation quality is improved for all tested language pairs, with the largest improvements in the (extreme lowresource) zero-shot directions.


This causes distance values to be close to 1 for any pair of longer words, even if they are actually very similar.

only slightly better than, say, the distance between sink [sINk] and song [sON].


Another important feature of document-level prediction is that certain parts of a document may be more important than others, such sentences containing keywords in a news article, versus sentences containing background information.

we use the QE dataset in , which i) is a superset of the official dataset for the wMT 2017 QE task and for which the POSTECH architecture has been developed, allowing us to validate our reimplementation; ii) contains translations from neural MT systems.


6 We believe that BI-RNN is able to better capture the fluency of NMT by encoding it directly as a sequence rather than assessing it word for word as POSTECH.

we experiment with sentence-level representations from either the POSTECH or our BI-RNN predictor.


1 to produce an utterance (W s ) corresponding to the current scene (O s ) and using a context appropriate portion (L) of the full lexicon.

the root causes of the effects of presentation order on word learning results should be understood as it may generalize to other cognitive tasks and can make up an important aspect of word learning models.


2 As our focus is on how parsers exploit the first examples of a training set, even just 10 sentences, we consider in this work only treebanks under 500 sentences.

uDPIPE notably seems to have troubles with determiners: not only does it achieve a lower score on DET dependencies, but it is also much slower learning those, compared to the other systems; uDPIPE consequently appears to under-exploit the determinism of that class.


By loosing the global constraints to local ones, the incorrect historical decisions that fall outside a window with size δ can never disturb a current prediction.

most transition-based neural models (Weiss et al., 2015;Zhou et al., 2015;Andor et al., 2016) incorporated the structured perceptron with beam search decoding, resulting in substantial improvement of accuracy.


Learning-rate schedule and early-stopping -The code base did not include an implementation of the learning-rate schedule that was reported to be used in the original experiments.

the exact configuration of the baseline is not discussed in the paper, however, the authors do share that they implement both layer-normalization and the output module.


We aim to shed light on the potential bottlenecks involved in re-purposing a complex deep learning sequence modeling architecture for computational linguistics studies.

they do not make the learned linguistic structure explicit: rather it can be presumed to be cryptically encoded in the states of the hidden layers.


we report our experimental results both with and without layer normalization.


(2016) showed that small batch sizes lead to more global and flat minimizers, while large batch sizes lead to more local and sharp minimizers.

we implement these two methods to use them as baselines for comparison.


This could be one of the reasons why models using CNN seem to have superior performance when only the best performance is reported.

the same strategy is adopted to our model when there are more than one Bi-LStM layers, in which case the input is added to the Bi-LStM layer's output.


Pre-trained Word Embeddings Utilizing word embeddings pre-trained on large unlabeled text has shown to be one of the most effective ways to increase performance on various NLP tasks.

starting from a small batch size and increasing it during training would result in a more global, but sharp minimizer.


(2017) have improved attention-based NMT by integrating a phrase memory, which stores target phrases provided by a statistical machine translation (SMT) (Pal et al., 2010) model, to perform a phrase-by-phrase translation rather than a word-by-word translation.

in Baseline Concat, the encoder generates a hidden state from the concatenated vector of the embedding vector for an input word and that for its NE tag at each time step (refer to Equation (9)).


In this section we evaluate our system along two dimensions -1.

in technical support, the multiple types of attributes necessitate their separate extraction and reasoning.


While this is the only error of this kind, we found several other sentence candidates similarly marked as containing the answer when they, in fact, do not.

cleaned, we were left with 1164 questions in the training set and 76 and 60 questions in the Raw and Clean versions of the development set respectively.


Thus, we proposed Knowledge-aware Attentive Network (KAN) for transfer learning on answer selection task.

knowledge module is responsible for transferring external knowledge from kB into the knowledgebased sentence representations.


• Mixed is to simply mix the training data from both target and source dataset to train the model.

obtaining rich annotated data is a time-consuming and expensive process, creating a substantial barrier for applying answer selection models to a new domain which has limited labeled data.


Recurrent networks provide a natural solution for modeling variable length sequences.

finally the prediction is given by a * = argmax c∈C p(c|D, Q).


dGR perform better or similarly to the GA reader ("dGR -(a) & (b) & (c)") in all categories except when query length is between 30 and 40 where GA reader wins with a small margin.


This reduces the task to sentence-level textual similarity as suggested by Vlachos and Riedel (2014) and implemented in ClaimBuster (Hassan et al., 2017b), Truthteller by The Washington Post 7 and one of the two modes of Full Fact's Live platform.

without considering these factors these approaches cannot be used to fact check information alone.


the broad range of types of claims in this dataset highlights a number of forms of misinformation to help identify the requirements for fact checking systems.


Motivated by the fact that later corrections are not as effective, the authors argued that the first post seen by a user is influential for their future opinion and thus it is important to show users rumours only once they are confirmed to be true.

note that this occurs in only two and one cases for baselines B1 and B2 respectively.


Further reducing sequences' length to five tweets leads to worse classification performance.


Next, we will investigate further the integration of temporal information for the rumour detection task.

this restricts the idea of stance-based rumour verification to only data where human stance labels are available.


Rumour veracity classification has also been studied in the RumourEval shared task at SemEval 2017 .

the most related studies to ours are Ma et al.


This takes a m dimensional document vector, d as a input, and produces a label, l, as Real(0) or Satire(1).

we can conclude that at the word level, semantic information is more relevant to capture satire than syntax information.


Of these, several studies have focused on the analysis of political leaning such as liberal or conservative (Bakshy et al., 2015;Wong et al., 2016) and the prediction of people's support for parties or candidates during the election period (Tumasjan et al., 2010;Burnap et al., 2016).

we calculate the number of topics for which users explicitly declared a stance.


Accordingly, the SemEval-2016 task 6B released unlabeled data on a topic to be predicted and labeled data on other topics (Mohammad et al., 2016).

we aim to predict stances of the silent majority by modeling the topic preferences of users with explicit stance statements and ordinary posts.


For instances, without stances stated (numbers of stances stated ≤ 0), methods that do not consider users' posts underperform the majority baseline.

it is expected that social media specific features such as users' follow-follower relationships, retweet relationships, or profile could be useful information for predicting stances.


While he doesn't appear to confirm or deny an affair, her reps said there is "no truth whatsoever" to the reports and labeled the situation "fabricated."

the CFG derived features consist of all the lexicalized production rules (rules including child nodes) combined with their parent and grandparent node, e.g., *NNˆNP→commission (in this example NN -a noun-is the grandparent node, NP -noun phrase-the parent node, and "commissions" the child node).


We are sensitive to issues that other researchers have in regards to Mechanical Turk Workers earning fair payment in exchange for their contributions to the HITs (Fort et al., 2011) .

from such evaluations and their own cross dataset evaluation, (Schneider et al., 2017) list the following common errors committed by OIE systems: • wrong boundaries around the arguments or predicate of a relation; • generation of redundant relations from the same sentence; • wrong extractions (e.g.


Indeed, as (Schneider et al., 2017) noted, there is little work on the evaluation of OIE systems.

we note that the most recent version of Open IE (version 5) is focused on n-ary relations.


However, with data sets such as SUD, these words are indicative of a moderate/strong negative sentiment.

• Amazon: This data set contains 1000 product reviews with labels 'positive' or 'negative'.


This result is not surprising, given that the pre-trained RNTN and CNNS are i)initialized with pre-trained word embeddings and ii)trained on a data set with roughly 10 times more training data points than within SWESA.

there are a total of 2049 distinct word tokens in this data set.


SWESA is geared towards small sized datasets, whereas neural network based algorithms are data intensive and can extract useful relationships from very large amounts of data.

note that on the imbalanced A-CHESS data set RNTN fails to perform any classification.


We also compare SWESA against an algorithm that uses convolutional neural networks to represent sentences (Kim, 2014) for sentiment analysis, and a sentiment analysis algorithm based on recurrent neural networks (Socher et al., 2013).

in the SUD application, as mentioned above, the amount of data that is available is limited and hence this approach is not a successful alternative.


When choosing λ = 0 (λ sg or λ sglr ) for our model, we obtain the same model as RC (SG init.

the skip-gram model is quite rough in its context description and does not take into account word order very well.


They alternately trained embeddings in a language model and a SRL model.

total frequency in the training data (D rc + D sg ) and made three subsets based on the average word frequency of the argument tokens in each relation.


As with other aspects of language, grammatical/prosodic influences from the native language may surface in the new language as particular punctuation choices.

we use a set of 36 punctuation marks (only 31 of which appear in the ICLE dataset).


In the previously mentioned experiments we focused on more abstract features (POS tags), which we combine with punctuation marks to capture a higher-level representation of punctuation usage.

while punctuation has been included in some of these studies (e.g., in character-level models), its impact has not been studied.


We worked on the Flickr30k entities dataset, where noun phrases are given and VGP supervision is available, extracting VGP in an end-to-end manner without supervision in other datasets such as the Microsoft COCO caption dataset (Chen et al., 2015) is a more realistic scenario and could be more interesting.

figure 4a shows such an example, where the SNN (fV+CCA) model fails to identify these two entities "a group of order men" and "a group of people" as VGPs due to the diverse textual descriptions of the the same visual concept.


We define VGPs as different phrasal expressions that describe the same visual concept in an image.

we apply the affinity propagation algorithm (Frey and Dueck, 2007) to cluster entities, which can estimate the number of clusters as well.


Elaboration  1208  36  9  Causality  365  29  20  Coordination  453 15 731 Table 9: Nuclearity distribution of discourse relationships.

an annotated document includes three parts: DISCOURSE, RELaTION and TEXT.


Alternative methods to modify tf*idf criteria (Lecorvé et al., 2008) could be used to increase the specificity of retrieved documents to  Figure 1: Impact of different stages of CCC on the various quality aspects of the generated content the topics in the query.

we used the gold standard summary for every case as the input snippet and the actual article as the reference.


A lower value of k results in the query under-representing the content and fetching articles that may not be very relevant.

once the candidates are generated, the rewards of the nodes in the graph are adjusted to account for the information in the generated candidates.


It should be noted, however, that subset/element-of pairs also have a lot in common with coreference pairs, since the lexical relation between their head nouns tends to be hypernymy, synonymy or plain word repetition (lexical relations which are summarised as lexical givenness in Baumann and Riester, 2012) or hyponymy (i.e.

the focus of this paper lies on full bridging resolution but we also report numbers for anaphor detection.


This is, for example, also reflected in the higher inter-annotator-agreement for definite than for indefinite bridging anaphors (Rösiger, 2018a).

compared to the original numbers in Hou et al.


For those who prefer the old Postville, Mayor John Hyman has a simple answer.

to ARRAU, SciCorp is an out-of-domain corpus annotated with referential bridging.


Our improvement on the Transformer model focuses on the last output layer of the decoder.

multi-task Learning Given N such non-fluent source word sequences, target word sequences and label sequences.


Different from (Cheng et al., 2016;Saha et al., 2016) which adopt the fully shared encoder, we share only partial weights for the encoders, considering that 1) The independent weights are devoted to encode the hidden features about the internal characteristics of each corpus, such as the terminology, style, and sentence structure; 2) The shared weights are concentrated on projecting those hidden features into the shared-latent space.

in the proposed model, we apply the self-attention layers to extract the hidden features of the input sentence.


It improves the best F-score of our self-attention based models from 89.2% to 91.1%, which indicates that our novel proposed weight sharing model is capable of learning useful language knowledge from unlabelled noisy dataset automatically.

we share the weights of the last few layers of two encoders that are responsible for extracting high-level representations of input sentences.


It is observed that the number of weight-sharing layers has a significant impact on the performance of disfluency detection.

the weight sharing constraint alone does not necessarily guarantee the same latent representations.


From the table, we can also observe that these two corpora -SWDA and AMI -contribute most to the performance, as removing them affects the performance the most.

secondly, we experiment with domain-independent DA tagging following the same approach and using our combined resource as a training corpus.


For the first experiment, the overall kappa is 0.9744.

we organized our experiment in blocks using diagrams with different subject matter.


object) feature embedding for a video; 2) Sentence generation network (Section 2.2), which uses CRF to learn optimal semantic representation and LSTM to generate the final description.

in this example, there are no reference captions that have same syntactic structure with our caption.


We can also find that LSTM-CSS and LSTM-CSS-E nearly perform equally.

the motion stream is a reproduced model of tSN, fine-tuned over MSR-Vtt-2016.


We deploy a conditional random fields layer as decoder.

taking Cardiology as an example, the size of source training set used in cross-medical (high quality) tasks is only 1 percent of that used in general-to-medical (high quantity) tasks, but the cross-medical results still outperform the latter.


In this section, we firstly describe a model used for addressee and response selection, and then explain our proposed methods to train parameters of the model.

in the following, we first explain our motivation for tackling M-ARS and then describe the formal task definitions.


To the best of our knowledge, this is the first work focusing on low-resource language enablement of conversational systems.

it is costly and impractical to adopt this evaluation method for multilingual systems, especially for minor-language systems.


To solve this, we maintain a set R(m i ) that is reachable from m i during the decoding process.

• Whether there are temporal expressions (AGM-TMP slot from a semantic parser (Tratz and Hovy, 2011)) in the sentences of the two mentions.


We will now look at some examples of corrections in the image description data.


It tends to skew low, especially when considering schemas.

this presupposes that better stability mirrors performance improvements, and that with more data, a germinator can better converge on the ground truth reflecting underlying knowledge about narratives in the news.


Sequence-to-sequence chit-chat skills generate several response candidates.

the error analysis showed that the user often asks complex questions that are not covered by the training set from SQuAD.


Basic analysis of this shows that some of this error is a consequence of the discussion genre: a baseline coreference model that did not track speakers in the AMRs makes important errors in coreference chains regarding "I" and "you".

such a score is limited in that it was measured over a very small exploration set (40 AMRs), but it provides some preliminary suggestion that the identical underlying AMRs used in the IAA numbers above are not dramatically inflating the inter-annotator agreement.


Representation based on internal words given an argument with words w 1 , w 2 , ..., w T , we first map each word to a dense vector obtaining x 1 , x 2 , ..., x T correspondingly.

we conduct an attention pooling operation over all the words to get the contribution of each word.


the top-ranked correct caption for each image.

joint embeddings joint embedding is a common technique for a wide range of tasks incorporating multiple domains, including audio-video embeddings for unsupervised representation learning (Ngiam et al., 2011), shape-image embeddings  for shape inference, bilingual word embeddings for machine translation (Zou et al., 2013), human pose-image embeddings for pose inference (Li, 2011), image-text embeddings for visual description (Reed et al., 2016), and global representation learning from multiple domains (Castrejon et al., 2016).


Given an image-caption pair and its corresponding textual adversarial samples, we are interested in the following question: what is the semantic distance between the image an adversarial caption?

flowers in a clear vase sitting on a table.


This further contributes to the success in numerous natural language processing (NLP) tasks such as language modeling (Cheng et al., 2016;Inan et al., 2017), sentiment analysis (Cheng et al., 2016;Kumar et al., 2016), and reading comprehension (Cheng et al., 2016;Chen et al., 2016;Shen et al., 2017).


The addition of these two constraints leads to significant increase in performance when used together.

we define the factor graph corresponding to the dependencies between stance predictions in a debate thread, and use the similarity between the embedded representation of objects as a scoring function for the factors.


As we see, the types of chitchat and logistics tend to be easily handled.

all of our models are run on a single GPU (GeForce GTX 1080 Ti).


A natural approach for multi-turn modeling is simply concatenating the context utterances (Lowe et al., 2015;.

improved the leveraging of utterances relationship and contextual information by matching a response with each utterance in the context based on a convolutional neural network.


To aggregate the matching information of the attentive turns in the last stage, The outputs of CNN, where GRU (•, •) denotes the same calculation and parameterization as Eq.

a first-stage turns-aware aggregation mechanism is proposed to address this problem.


Table 2-3 show the results on the three corpora.

this is quite labour-intensive and subjective.


Selecting content in terms of argumentative discourse units (along with facts, anecdotes, and similar) that frame the given topic in a way that is effective for the intended stance, 2. arranging the structure of the units considering ordering preferences, and 3. phrasing the style of the resulting text to match the genre and the encoded means of persuasion.

table 6: Differences between strategies in the relative frequency and the rank of the three most common unit type flows in the synthesized arguments.


As the only daughter of Nigerian immigrants with a tenuous-at-best toehold on the middle class, college was billed as the only path to financial security.

in total, our main contributions are: 1.


About half of all texts begin with the thesis, whereas pro units rather come at the end.

we kept only those with the majority stance on the topic, resulting in at least four theses for each of the 10 topics.


We summarize the performance of the two models in Table 8.

we evaluate a standard sequence-to-sequence model with an attention mechanism (Bahdanau et al., 2015) and a hierarchical recurrent encoder-decoder model (Serban et al., 2016).


Seq2Seq models map dialogue history directly into the response in current turn, while requires a minimum amount of hand-crafting.

these response are based on distinct dialogue history in navigation test data.


Each state representation u IN k is calculated by an attention over the whole representation of history: where we assign each hidden state m different kinds of scores and perform a weighted average.

their modules like belief tracker still needs to be trained separately before end-to-end training.


But in these web snippets text varies widely, viz.,"Meeting schedules, contact information, calendar of events, ftm, mtf, soffa, information, articles, book catalog, and guestbook."

it is evident from the example that there is complimentary information in the images that is either unavailable in the text or can be in contrast to the text.


Parent-Branch Tree-LSTM, Child-Sum Tree-LSTM, and GCN are used as components of the proposed model to capture discussion structures.

the next preferred component is Child-Sum Tree-LSTM, which performed quite poorly as a single component model.


While this may be perceived as a disadvantage of the current proposal, it is in fact its advantage, as it increases the internal consistency of UD: the treatment of broadly nominal dependents is fully analogous.

since there are no generally accepted objecthood criteria of clausal dependents of verbs, only those clausal dependents should be treated as objects which may be coordinated with nominal -i.e., uncontroversial -objects.


The second sentence, making reference to the notion of object, could in principle avoid AAD, if there were independent criteria of deciding when a clausal dependent "functions like an object".

note that the UD guidelines are actually inconsistent about the status of the immediately post-verbal nP in double object constructions.


The current UD practice follows the linguistic tradition: indirect objects are those NPs in double object constructions which bear the semantic role of a recipient or a benefactor.

so, for example, in the attested 12 (30), the 'closed' clausal conjunct should be analysed as a direct object, because the nominal conjunct, balance, is marked as a direct object (and the preceding NP, me, is marked as an indirect object): as is often the case with linguistic tradition, this traditional view confuses different levels of linguistic representation, namely, semantic roles and (syntactic) grammatical functions.


Inclusion in a resource thus is not a very useful feature as it is only predictive of the majority class.

for -könig the reverse situation holds in a statistically significant way: the frequencies of affixoid formations are higher than those of the non-affixoid formations.


Once the training is finished, we select the model with the best micro-F 1 score on the validation set as our final model and evaluate its performance on the test set.

in order to model label correlations, label powerset (LP) (Tsoumakas and Katakis, 2006) transforms a multi-label problem to a multi-class problem with a classifier trained on all unique label combinations.


Given the label space with L labels , a text sequence x containing m words, the task is to assign a subset y containing n labels in the label space L to x.

the sorting of labels is very important for the models' performance.


Previous research approached the task of determining which rule combinations to allow either by a process of manual trial and error or by statistical measures based on a collection of positive examples only: if the original grammar produces more than a single parse of a sentence, only the "correct" parse was stored in the treebank.

lines 3 and 4 list results for fully specialized grammars.


The implementation used here features several enhancements over the original Dedina and Nusbaum (D&N) system (Marchand and Damper, 2000).

the best-scoring pronunciation is then selected as output.


(1999b) for details) and one instantiation of maccent, since the current version does not implement many variations.

we simply took the features suggested in that paper, and 2 different settings for our MBL implementation, IGTREE and MVDM K----5, the latter being the optimal algorithm for the previous experiments.


We adopted the modified value difference metric (MVDM) to assign a different distance between each pair of values of the same feature.

the taggers need to be emulated using symbolic learners and a preprocessing front-end to translate the corpus data into feature value vectors.


Given this, a generic system is required to facilitate the analysis of behavioural trends amongst selected pairs of terminals and non-terminals alike, regardless of the target language.

using a five thousand-word extract from the LOB corpus (Johansson et al., 1986) to test this tool, a number of parts-of-speech pairings were analysed for their cohesive profiles.


This then allowed us to segment the signal into words-like units ('words' for simplicity).

we feel that by deliberately reducing our dependence on prior knowledge ('increasing our ignorance of language') and by treating language as a 'signal', we might be contributing a novel approach to natural language processing which might ultimately lead to a better, more fundamental understanding of what distinguishes language from the rest of the signal universe.


SNoW is proven to perform very well in high dimensional NLP problems, where both the training examples and the target function reside very sparsely in the feature space (Roth, 1998), e.g: context-sensitive spelling correction, POS tagging, PP-attachment disambiguation, etc.

the sign of the prediction of many of these common rules was somewhat contradictory between corpora.


It can be shown (Della Pietra et al., 1995) that these are the also the values which minimize the Kullback-Liebler divergence D(p[[q) between the model and the reference distribution under the constraint that the expectations of the features (ie, the components of f) with respect to the model must equal their expectations with respect to the empirical distribution derived from the training corpus.

a simple solution to this dilemma is to let the value of a word-pair feature reflect the current position of the pair rather just its presence or absence.


Because different corpora are used to obtain the various results reported in the literature and the problem is often defined differently, detailed comparison is difficult.

another important application is in the field known as augmentative and alternative communication (aaC).


The next step is to characterize all events in the data in terms of those features.

its peak at approximately 42.5% performance comes early, at around 20 iterations of iiS, and subsequently drops to 40.5% at around 50 iterations.


Let E(Si) represent the expected number of sentences that will be consumed in state Si.

let Di(x) denote the probability distribution of expression of the input sample.


can be applied to shape an abstract input domain for almost any learning strategy.

i believe a three-fold approach to validate a computational model of acquisition is warranted.


Our algorithm establishes a list which identifies the rulesets for every hypothesized rule extracted from the data and then it must proceed to determine which rulesets or PPMVs describe true morphological relationships.

his overall goal is different from ours: he primarily seeks an affix inventory.


He applies the EM algorithm to eliminate inappropriate parses.


On its own, the method basically inherits a part of the detrimental "forgetting exceptions is harmful" effect from its ruleinduction source (this effect is stronger when % Correct test instances Task   IBI-IG  RBM  IBi-IG-bRBM   CAR 93.9 ± 2.1 98.9 ± 0.8 97.2 ± 1.3 NURSERY 94.6 ± 0.6 98.6 ± 0.5 98.7 ± 0.2 SPLICE 91.7 ± 1.1 89.0 ± 2.1 92.7 ± 1.7 Table 5: Average generalisation accuracies of IBi-IG, RIPPER, and RBM on three machinelearning benchmark tasks.

rIPPEr's ability to represent sets of values at left-hand side conditions, and its flexibility in producing larger or smaller numbers of rules.


Now when instances are represented this way, they can be used in k-NN classification.

adding the (average) 971 complex features to the nine multi-valued features in the GPSM causes a slight drop in performance -and a slowdown.


Furthermore, the WSME model is easier to train than the conditional one, because in the WSME model we don't need to estimate the normalization constant Z during the training time.

in order to incorporate long-distance information into the ME framework in a language model, a Whole Sentence Maximum Entropy Language Model (WSME) could be used.


We make union of all chunk's clusters except one tested cluster and then intersect this one with chunk's union.

with the mentioned methodologies, we are interested in finding a limited set of domain independent words (less than 1000) including prepositions, adverbs and adjectives and using these words for unsupervised clustering and automatic extraction of the relevant knowledge from dialog corpus.


So far they were run to find the closest match of held-out corpus sentences using the syntactic structure and the performance structure, for each of the distance metrics.

this is still time-consuming, and we are trying to define a general way to limit the search in such a tree structure space, for example based on tree indexing for efficiency (Daelemans et al., 1997).


The kNN algorithm was tested with values of k equal to 1, 2, 5 and 8, being 5 the optimal number of neighbors.

these three last works do not consider any set of heuristics for UCE filtering.


For all variants of MBLP discussed here, feature selection can also improve both accuracy and efficiency by discarding some features altogether because of their irrelevance or even counter-productivity in learning to solve the task.

we have found no results (on these particular data) that indicate an advantage of evolutionary feature selection approach over the more classical iterative methods.


The PP attachment task (PP) is prepositional phrase attachment to either a preceding verb or a preceding noun, on the basis of the verb, the noun, the preposition in question and the head noun of the prepositional complement.

a more pragmatic approach will have to be taken.


This fact is in agreement with the observation put forward by Charniak (Charniak, 1996) that simple PCFGs, directly obtained from a corpus, largely overgeneralize.

as expected, the number of rules obtained increases as more information is conveyed by the node label, although this increase is not extreme.


the most successful was an application of the Alembic parser which uses transformation-based rules.


The idea is to build K classifiers that separate one class among from all others.

for the features, we decided to use all the information available in the surrounding contexts, such as the words, their POS tags as well as the chunk labels.


The full version of this paper can be found at http://www.cogsci.ed.ac.uk/'osborne/shallow.ps The insight here is that one can view (some of) the differences between tagging and (shallow) parsing as one of context: shallow parsing requires access to a greater part of the surrounding lexical/POS syntactic environment than does simple POS tagging.

an additional source of errors were odd annotation decisions.


This dictionary gave us the possible lexical tags for each word from the corpus.

in the specialized iLM, a state was specified for a certain word Wk (only if the Wk word belongs to the category Ci).